{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Node representation learning with attri2vec"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbsphinx": "hidden",
    "tags": [
     "CloudRunner"
    ]
   },
   "source": [
    "<table><tr><td>Run the latest release of this notebook:</td><td><a href=\"https://mybinder.org/v2/gh/stellargraph/stellargraph/master?urlpath=lab/tree/demos/embeddings/attri2vec-embeddings.ipynb\" alt=\"Open In Binder\" target=\"_parent\"><img src=\"https://mybinder.org/badge_logo.svg\"/></a></td><td><a href=\"https://colab.research.google.com/github/stellargraph/stellargraph/blob/master/demos/embeddings/attri2vec-embeddings.ipynb\" alt=\"Open In Colab\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\"/></a></td></tr></table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is the python implementation of the attri2vec algorithm outlined in paper [1]. The implementation uses the stellargraph components.\n",
    "\n",
    "<a name=\"refs\"></a>\n",
    "**References:** \n",
    "\n",
    "[1] [Attributed Network Embedding via Subspace Discovery](https://link.springer.com/article/10.1007/s10618-019-00650-2). D. Zhang, Y. Jie, X. Zhu and C. Zhang, Data Mining and Knowledge Discovery, 2019. \n",
    "\n",
    "## attri2vec\n",
    "\n",
    "attri2vec learns node representations by performing a linear/non-linear mapping on node content attributes. To make the learned node representations respect structural similarity, [DeepWalk](https://dl.acm.org/citation.cfm?id=2623732)/[Node2Vec](https://snap.stanford.edu/node2vec) learning mechanism is used to make nodes sharing similar random walk context nodes represented closely in the subspace. \n",
    "\n",
    "For each (``target``,``context``) node pair $(v_i,v_j)$ collected from random walks,  attri2vec learns the representation for the target node $v_i$ by using it to predict the existence of context node $v_j$, with the following three-layer neural network."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](attri2vec-illustration.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Node $v_i$'s representation in the hidden layer is obtained by multiplying $v_i$'s raw content feature vector in the input layer with the input-to-hidden weight matrix $W_{in}$ followed by an activation function. To obtain the hidden-layer representation, the one-layer mapping can be replaced by multi-layer mappings. The existence probability of each node conditioned on node $v_i$ is outputted in the output layer, which is obtained by multiplying $v_i$'s hidden-layer representation with the hidden-to-out weight matrix $W_{out}$ followed by a softmax activation. To capture the ``target-context`` relation between $v_i$ and $v_j$, we need to maximize the probability $\\mathrm{P}(v_j|v_i)$. However, computing $\\mathrm{P}(v_j|v_i)$ is time consuming, which involves the matrix multiplication between $v_i$'s hidden-layer representation and the hidden-to-out weight matrix $W_{out}$."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To speed up the computing, we adopt the negative sampling strategy [1]. For each (``target``, ``context``) node pair, we sample a negative node $v_k$, which is not $v_i$'s context. To obtain the output, instead of multiplying $v_i$'s hidden-layer representation with the hidden-to-out weight matrix $W_{out}$ followed by a softmax activation, we only calculate the dot product between $v_i$'s hidden-layer representation and the $j$th column as well as the $k$th column of the hidden-to-output weight matrix $W_{out}$ followed by a sigmoid activation respectively. According to [1], the original objective to maximize $\\mathrm{P}(v_j|v_i)$ can be approximated by minimizing the cross entropy between $v_j$ and $v_k$'s outputs and their ground-truth labels (1 for $v_j$ and 0 for $v_k$)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The entire model is trained end-to-end by minimizing the binary cross-entropy loss function with regards to predicted node pair labels and true node pair labels, using stochastic gradient descent (SGD) updates of the model parameters, with minibatches of 'training' node pairs generated on demand and fed into the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "nbsphinx": "hidden",
    "tags": [
     "CloudRunner"
    ]
   },
   "outputs": [],
   "source": [
    "# install StellarGraph if running on Google Colab\n",
    "import sys\n",
    "if 'google.colab' in sys.modules:\n",
    "  %pip install -q stellargraph[demos]==1.2.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "nbsphinx": "hidden",
    "tags": [
     "VersionCheck"
    ]
   },
   "outputs": [],
   "source": [
    "# verify that we're using the correct version of StellarGraph for this notebook\n",
    "import stellargraph as sg\n",
    "\n",
    "try:\n",
    "    sg.utils.validate_notebook_version(\"1.2.1\")\n",
    "except AttributeError:\n",
    "    raise ValueError(\n",
    "        f\"This notebook requires StellarGraph version 1.2.1, but a different version {sg.__version__} is installed.  Please see <https://github.com/stellargraph/stellargraph/issues/1172>.\"\n",
    "    ) from None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import networkx as nx\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "import random\n",
    "\n",
    "import stellargraph as sg\n",
    "from stellargraph.data import UnsupervisedSampler\n",
    "from stellargraph.mapper import Attri2VecLinkGenerator, Attri2VecNodeGenerator\n",
    "from stellargraph.layer import Attri2Vec, link_classification\n",
    "\n",
    "from tensorflow import keras\n",
    "\n",
    "from pandas.core.indexes.base import Index\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.manifold import TSNE\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegressionCV\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "from stellargraph import datasets\n",
    "from IPython.display import display, HTML"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataset\n",
    "\n",
    "For clarity we ignore isolated nodes and subgraphs and use only the largest connected component."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "DataLoadingLinks"
    ]
   },
   "source": [
    "(See [the \"Loading from Pandas\" demo](../basics/loading-pandas.ipynb) for details on how data can be loaded.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": [
     "DataLoading"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "The CiteSeer dataset consists of 3312 scientific publications classified into one of six classes. The citation network consists of 4732 links, although 17 of these have a source or target publication that isn't in the dataset and only 4715 are included in the graph. Each publication in the dataset is described by a 0/1-valued word vector indicating the absence/presence of the corresponding word from the dictionary. The dictionary consists of 3703 unique words."
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset = datasets.CiteSeer()\n",
    "display(HTML(dataset.description))\n",
    "G, subjects = dataset.load(largest_connected_component_only=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "StellarGraph: Undirected multigraph\n",
      " Nodes: 2110, Edges: 3757\n",
      "\n",
      " Node types:\n",
      "  paper: [2110]\n",
      "    Edge types: paper-cites->paper\n",
      "\n",
      " Edge types:\n",
      "    paper-cites->paper: [3757]\n"
     ]
    }
   ],
   "source": [
    "print(G.info())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train attri2vec on Citeseer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Specify the other optional parameter values: root nodes, the number of walks to take per node, the length of each walk."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "nodes = list(G.nodes())\n",
    "number_of_walks = 4\n",
    "length = 5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create the UnsupervisedSampler instance with the relevant parameters passed to it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "unsupervised_samples = UnsupervisedSampler(\n",
    "    G, nodes=nodes, length=length, number_of_walks=number_of_walks\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set the batch size and the number of epochs. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 50\n",
    "epochs = 4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define an attri2vec training generator, which generates a batch of (feature of target node, index of context node, label of node pair) pairs per iteration."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "generator = Attri2VecLinkGenerator(G, batch_size)\n",
    "train_gen = generator.flow(unsupervised_samples)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Building the model: a 1-hidden-layer node representation ('input embedding') of the `target` node and the parameter vector ('output embedding') for predicting the existence of `context node` for each `(target context)` pair, with a link classification layer performed on the dot product of the 'input embedding' of the `target` node and the 'output embedding' of the `context` node.\n",
    "\n",
    "Attri2Vec part of the model, with a 128-dimension hidden layer, no bias term and no normalization. (Normalization can be set to 'l2'). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "layer_sizes = [128]\n",
    "attri2vec = Attri2Vec(\n",
    "    layer_sizes=layer_sizes, generator=generator, bias=False, normalize=None\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Build the model and expose input and output sockets of attri2vec, for node pair inputs:\n",
    "x_inp, x_out = attri2vec.in_out_tensors()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use the link_classification function to generate the prediction, with the `ip` edge embedding generation method and the `sigmoid` activation, which actually performs the dot product of the 'input embedding' of the target node and the 'output embedding' of the context node followed by a sigmoid activation. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "link_classification: using 'ip' method to combine node embeddings into edge embeddings\n"
     ]
    }
   ],
   "source": [
    "prediction = link_classification(\n",
    "    output_dim=1, output_act=\"sigmoid\", edge_embedding_method=\"ip\"\n",
    ")(x_out)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Stack the Attri2Vec encoder and prediction layer into a Keras model, and specify the loss."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = keras.Model(inputs=x_inp, outputs=prediction)\n",
    "\n",
    "model.compile(\n",
    "    optimizer=keras.optimizers.Adam(lr=1e-3),\n",
    "    loss=keras.losses.binary_crossentropy,\n",
    "    metrics=[keras.metrics.binary_accuracy],\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Train the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/4\n",
      "1351/1351 - 25s - loss: 0.6847 - binary_accuracy: 0.5540\n",
      "Epoch 2/4\n",
      "1351/1351 - 27s - loss: 0.5482 - binary_accuracy: 0.7233\n",
      "Epoch 3/4\n",
      "1351/1351 - 31s - loss: 0.3638 - binary_accuracy: 0.8644\n",
      "Epoch 4/4\n",
      "1351/1351 - 31s - loss: 0.2639 - binary_accuracy: 0.9109\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(\n",
    "    train_gen,\n",
    "    epochs=epochs,\n",
    "    verbose=2,\n",
    "    use_multiprocessing=False,\n",
    "    workers=1,\n",
    "    shuffle=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Visualise Node Embeddings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Build the node based model for predicting node representations from node content attributes with the learned parameters. Below a Keras model is constructed, with `x_inp[0]` as input and `x_out[0]` as output. Note that this model's weights are the same as those of the corresponding node encoder in the previously trained node pair classifier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_inp_src = x_inp[0]\n",
    "x_out_src = x_out[0]\n",
    "embedding_model = keras.Model(inputs=x_inp_src, outputs=x_out_src)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get the node embeddings by applying the learned mapping function to node content features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "43/43 [==============================] - 0s 3ms/step\n"
     ]
    }
   ],
   "source": [
    "node_gen = Attri2VecNodeGenerator(G, batch_size).flow(subjects.index)\n",
    "node_embeddings = embedding_model.predict(node_gen, workers=1, verbose=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Transform the embeddings to 2d space for visualisation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "transform = TSNE  # PCA\n",
    "\n",
    "trans = transform(n_components=2)\n",
    "node_embeddings_2d = trans.fit_transform(node_embeddings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# draw the embedding points, coloring them by the target label (paper subject)\n",
    "alpha = 0.7\n",
    "label_map = {l: i for i, l in enumerate(np.unique(subjects))}\n",
    "node_colours = [label_map[target] for target in subjects]\n",
    "\n",
    "plt.figure(figsize=(7, 7))\n",
    "plt.axes().set(aspect=\"equal\")\n",
    "plt.scatter(\n",
    "    node_embeddings_2d[:, 0],\n",
    "    node_embeddings_2d[:, 1],\n",
    "    c=node_colours,\n",
    "    cmap=\"jet\",\n",
    "    alpha=alpha,\n",
    ")\n",
    "plt.title(\"{} visualization of node embeddings\".format(transform.__name__))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Downstream Task"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The node representations learned by attri2vec can be used as features for downstream tasks, such as node classification, community detection, and link prediction. As attri2vec inductively learns a mapping function from node content features to the target embeddings, it has the ability to infer the representations for out-of-sample nodes, which can be used to predict the labels or links of out-of-sample nodes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbsphinx": "hidden",
    "tags": [
     "CloudRunner"
    ]
   },
   "source": [
    "<table><tr><td>Run the latest release of this notebook:</td><td><a href=\"https://mybinder.org/v2/gh/stellargraph/stellargraph/master?urlpath=lab/tree/demos/embeddings/attri2vec-embeddings.ipynb\" alt=\"Open In Binder\" target=\"_parent\"><img src=\"https://mybinder.org/badge_logo.svg\"/></a></td><td><a href=\"https://colab.research.google.com/github/stellargraph/stellargraph/blob/master/demos/embeddings/attri2vec-embeddings.ipynb\" alt=\"Open In Colab\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\"/></a></td></tr></table>"
   ]
  }
 ],
 "metadata": {
  "file_extension": ".py",
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.9"
  },
  "mimetype": "text/x-python",
  "name": "python",
  "npconvert_exporter": "python",
  "pygments_lexer": "ipython3",
  "version": 3
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
